Data-driven assisted real-time optimal control strategy of submerged arc furnace via intelligent energy terminals considering large-scale renewable energy utilization.

ABSTRACT: This study presents a data-driven assisted real-time optimization model which is an innovative approach to address the challenges posed by integrating Submerged Arc Furnace (SAF) systems with renewable energy sources, specifically photovoltaic (PV) and wind power, with modern intelligent energy terminals. Specifically, the proposed method is divided into two stages. The first stage is related to data-driven prediction for addressing local time-varying renewable energy and electricity market prices with predicted information, and the second stage uses an optimization model for real-time SAF dispatch. Connections between intelligent energy terminals, demand-side devices, and load management systems are established to enhance local renewable resource utilization. Additionally, mathematical fo

SUBMITTER: Zheng B 

PROVIDER: S-EPMC10918093 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature